An adaptable and scalable asymmetric cryptographic processor

In this paper a novel scalable public-key processor architecture is presented that supports modular exponentiation and Elliptic Curve Cryptography over both prime GF(p) and binary GF(2) extension fields. This is achieved by a high performance instruction set that provides a comprehensive range of integer and polynomial basis field arithmetic. The instruction set and associated hardware are generic in nature and do not specifically support any cryptographic algorithms or protocols. Firmware within the device is used to efficiently implement complex and data intensive arithmetic. A firmware library has been developed in order to demonstrate support for numerous exponentiation and ECC approaches, such as different coordinate systems and integer recoding methods. The processor has been developed as a high-performance asymmetric cryptography platform in the form of a scalable Verilog RTL core. Various features of the processor may be scaled, such as the pipeline width and local memory subsystem, in order to suit area, speed and power requirements. The processor is evaluated and compares favourably with previous work in terms of performance while offering an unparalleled degree of flexibility. © 2006 IEEE.

Errorless learning and memory performance in schizophrenia

There is evidence that patients with schizophrenia have impaired explicit memory and intact implicit memory. The present study sought to replicate and extend that of O'Carroll et al. [O'Carroll, R.E., Russell, H.H., Lawrie, S.M. and Johnstone, E.C., 1999. Errorless learning and the cognitive rehabilitation of memory-impaired schizophrenic patients. Psychological Medicine 29, 105-112.] which reported that for memory-impaired patients with schizophrenia performance on a (cued) word recall task is enhanced using errorless learning techniques (in which errors are prevented during learning) compared to errorful learning (the traditional trial-and-error approach). Thirty patients with a DSM-IV diagnosis of schizophrenia and fifteen healthy controls (HC) participated. The Rivermead Behavioural Memory Test was administered and from their scores, the schizophrenic patients were classified as either memory-impaired (MIS), or memory-unimpaired (MUS). During the training phase two lists of words were learned separately, one using the errorless learning approach and the other using an errorful approach. Subjects were then tested for their recall of the words using cued recall. After errorful learning training, performance on word recall for the MIS group was impaired compared to the MUS and HC groups. However, after errorless learning training, no significant differences in performance were found between the three groups. Errorless learning may play an important role in remediation of cognitive deficits for patients with schizophrenia. (c) 2007 Elsevier Ireland Ltd. All rights reserved.

Abnormal Functional Connectivity of Hippocampus During Episodic Memory Retrieval Processing Network in Amnestic Mild Cognitive Impairment.

BACKGROUND: Functional connectivity magnetic resonance imaging technique has revealed the importance of distributed network structures in higher cognitive processes in the human brain. The hippocampus has a key role in a distributed network supporting memory encoding and retrieval. Hippocampal dysfunction is a recurrent finding in memory disorders of aging such as amnestic mild cognitive impairment (aMCI) in which learning- and memory-related cognitive abilities are the predominant impairment. The functional connectivity method provides a novel approach in our attempts to better understand the changes occurring in this structure in aMCI patients. METHODS: Functional connectivity analysis was used to examine episodic memory retrieval networks in vivo in twenty 28 aMCI patients and 23 well-matched control subjects, specifically between the hippocampal structures and other brain regions. RESULTS: Compared with control subjects, aMCI patients showed significantly lower hippocampus functional connectivity in a network involving prefrontal lobe, temporal lobe, parietal lobe, and cerebellum, and higher functional connectivity to more diffuse areas of the brain than normal aging control subjects. In addition, those regions associated with increased functional connectivity with the hippocampus demonstrated a significantly negative correlation to episodic memory performance. CONCLUSIONS: aMCI patients displayed altered patterns of functional connectivity during memory retrieval. The degree of this disturbance appears to be related to level of impairment of processes involved in memory function. Because aMCI is a putative prodromal syndrome to Alzheimer's disease (AD), these early changes in functional connectivity involving the hippocampus may yield important new data to predict whether a patient will eventually develop AD.